THE AUDITORY MODELING TOOLBOX
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ZILANY2014 - Auditory-nerve filterbank (improved)
Program code:
function [r_mean,psth,ihc,c1,c2,r_var,output] = zilany2014(stim,fsstim,fc,varargin)
%ZILANY2014 Auditory-nerve filterbank (improved)
%
% Usage: [ANresp,fc] = zilany2014(lvl,stim,fsstim);
% [ANresp,fc,psth,ihc,t_var] = zilany2014(lvl,stim,fsstim);
% [ANresp,fc,psth,ihc,t_var] = zilany2014(lvl,stim,fsstim);
%
% Input parameters:
% stim : Pressure waveform of stimulus (timeseries)
% fsstim : Sampling frequency of stimulus
% fc : Frequency vector containing the CFs.
% Use fc=audspace(lo,hi,numCF,'erb'); to space equally on the
% ERB frequency scale.
%
% Output parameters:
% r_mean : Instantaneous mean spiking rate (incl. refractoriness)
% of different AN fibers at corresponding CFs. Size: [time CFs]
% psth : Spike histogram
% ihc : Output from inner hair cells (IHCs) in Volts
% c1 : Output from the chirping filter C1
% c2 : Output from the chirping filter C2
% r_var : Instananeous variance in the discharge rate of the ANs
%
%
% This function takes the following optional key/value pairs:
%
% 'fsmod',fsmod Model sampling rate. It is possible to run the model
% at a range of fsmod between 100 kHz and 500 kHz.
% Default value is 200kHz for cats and 100kHz for humans.
%
% 'fiberType',fT Type of the fiber based on spontaneous rate (SR)
% 1: Low SR, SR fixed to 0.1 spikes/s
% 2: Medium SR, SR fixed to 4 spikes/s
% 3: High SR, SR fixed to 100 spikes/s
% 4: Custom, defined by the fibre numbers in numH, numM and numL
%
% 'numH' Number of high SR fibres. Only if fiberType is 4.
% 'numM' Number of medium SR fibres. Only if fiberType is 4.
% 'numL' Number of low SR fibres. Only if fiberType is 4.
%
% 'cohc',cohc OHC scaling factor: 1 denotes normal OHC function (default);
% 0 denotes complete OHC dysfunction.
%
% 'cihc',cihc IHC scaling factor: 1 denotes normal IHC function (default);
% 0 denotes complete IHC dysfunction.
%
% 'nrep',nrep Number of repetitions for the mean rate,
% rate variance & psth calculation.
%
% ZILANY2014 accepts the following flag:
%
% 'human' Use model parameters for humans. This is the default.
%
% 'cat' Use model parameters for cats.
%
% 'fixedFGn' Fractional Gaussian noise will be the same in every
% simulation. This is the default.
%
% 'varFGn' Fractional Gaussian noise will be different in every
% simulation.
%
% 'approxPL' Use approxiate implementation of the power-law
% functions. This is the default.
%
% 'actualPL' Use actual implementation of the power-law functions.
%
% 'shera2002' Selects the BM tuning from Shera et al. (2002) (default).
%
% 'glasberg1990' Selects the BM tuning from Glasberg & Moore (1990)
%
% ZILANY2014(...) returns modeled responses of multiple AN fibers tuned to
% various characteristic frequencies (CFs). Middle-ear filtering is
% included and corresponds to middleearfilter(...,'zilany2009');
% Please cite the references below if you use this model.
%
% See also: demo_zilany2014 zilany2014_synapse zilany2014_innerhaircells
% zilany2014_ffgn zilany2014 zilany2007
% data_baumgartner2016 plot_roenne2012 plot_roenne2012_chirp
% plot_roenne2012_tonebursts demo_carney2015 baumgartner2016_spectralanalysis
% roenne2012_click roenne2012_chirp carney2015_generateneurogram
% roenne2012_tonebursts exp_osses2022
% bruce2018 roenne2012 baumgartner2013
% baumgartner2016
%
% Demos: demo_zilany2014
%
% References:
% C. A. Shera, J. J. J. Guinan, and O. A. J. Revised estimates of human
% cochlear tuning from otoacoustic and behavioral measurements.
% Proceedings of the National Academy of Sciences of the United States of
% America, 99(5):3318--3323, 2002.
%
% B. R. Glasberg and B. Moore. Derivation of auditory filter shapes from
% notched-noise data. Hearing Research, 47(1-2):103--138, 1990.
%
% M. S. A. Zilany, I. C. Bruce, and L. H. Carney. Updated parameters and
% expanded simulation options for a model of the auditory periphery. The
% Journal of the Acoustical Society of America, 135(1):283--286, Jan.
% 2014.
%
% M. Zilany, I. Bruce, P. Nelson, and L. Carney. A phenomenological model
% of the synapse between the inner hair cell and auditory nerve:
% Long-term adaptation with power-law dynamics. J. Acoust. Soc. Am.,
% 126(5):2390 -- 2412, 2009.
%
%
% Url: http://amtoolbox.org/amt-1.1.0/doc/models/zilany2014.php
% Copyright (C) 2009-2021 Piotr Majdak, Clara Hollomey, and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.1.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% #StatusDoc: Good
% #StatusCode: Good
% #Verification: Unknown
% #Requirements: MATLAB MEX M-Signal
% #Author: Muhammad Zilany
% #Author: Robert Baumgartner: adapted to the AMT
% #Author: Piotr Majdak (2021): C1 and C2 outputs
if nargin<3
error('%s: Too few input parameters.',upper(mfilename));
end;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Loading default values, reading input arguments:
if nargout >= 7
output = [];
end
% Define input flags and values
definput.import = {'zilany2014'}; % load defaults from arg_zilany2014
[flags,kv] = ltfatarghelper({'fsmod','fiberType','cohc','cihc','nrep'},definput,varargin);
% Species settings
if flags.do_cat, kv.fsmod = max(kv.fsmod,200e3); end
% Stimulus settings
stim = resample(stim,kv.fsmod,fsstim); % stim fs = mod fs
fs = kv.fsmod;
tdres = 1/fs; % time interval of processing (in s), i.e., reciprocal of the sampling rate
stim = stim(:)'; % stim will be a row vector
numCF=length(fc);
% reptime is the time between stimulus repetitions in seconds; -> set
% twice the duration of stim
reptime = kv.reptime*length(stim)/fs;
% species must be 1 for cat, 2 for human (shera2002) or 3 for human (glasberg1990)
if flags.do_cat, species=1; end
if flags.do_human
if flags.do_shera2002, species=2; end
if flags.do_glasberg1990, species=3; end
end
% noiseType is for fixed fGn (noise will be same in every simulation) or variable fGn: "0" for fixed fGn and "1" for variable fGn
noiseType = flags.do_varFGn + 0;
% implnt is for "approxiate" or "actual" implementation of the power-law functions: "0" for approx. and "1" for actual implementation
implnt = flags.do_actualPL + 0;
% Call AN model and loop for all fibers tuned to different CFs
ihc = zeros(round(reptime*fs)*kv.nrep,numCF);
r_mean = zeros(round(reptime*fs),numCF);
r_var = zeros(round(reptime*fs),numCF);
psth = zeros(round(reptime*fs),numCF);
if nargout >= 7
% Memory allocation:
meanrate_LSR = zeros(size(r_mean));
meanrate_MSR = zeros(size(r_mean));
meanrate_HSR = zeros(size(r_mean));
psth_LSR = zeros(size(psth));
psth_MSR = zeros(size(psth));
psth_HSR = zeros(size(psth));
end
if nargout>=5,
c1 = zeros(round(reptime*fs*kv.nrep),numCF);
c2 = zeros(round(reptime*fs*kv.nrep),numCF);
end
if kv.fiberType==4,
fiberTypes=[ones(kv.numL,1);ones(kv.numM,1)*2;ones(kv.numH,1)*3]; % custom fiber types
else
fiberTypes=kv.fiberType;
end
for jj = 1:numCF
if nargout>=5,
[ihc(:,jj),c1(:,jj),c2(:,jj)] = zilany2014_innerhaircells(stim,fc(jj),kv.nrep,tdres,reptime,kv.cohc,kv.cihc,species);
else
ihc(:,jj) = zilany2014_innerhaircells(stim,fc(jj),kv.nrep,tdres,reptime,kv.cohc,kv.cihc,species);
end
for ii=1:length(fiberTypes)
amt_disp(['CF = ' int2str(jj) '/' int2str(numCF) '; spont = ' int2str(ii) '/' int2str(length(fiberTypes))],'volatile');
[r_mean1,r_var1,psth1] = zilany2014_synapse(ihc(:,jj)',fc(jj),kv.nrep,tdres,fiberTypes(ii),noiseType,implnt);
r_mean(:,jj)=r_mean(:,jj)+r_mean1;
r_var(:,jj)=r_var(:,jj)+r_var1;
psth(:,jj)=psth(:,jj)+psth1;
if nargout >= 7
switch fiberTypes(ii)
case 1
meanrate_LSR(:,jj) = meanrate_LSR(:,jj)+r_mean1;
psth_LSR(:,jj) = psth_LSR(:,jj)+psth1;
case 2
meanrate_MSR(:,jj) = meanrate_MSR(:,jj)+r_mean1;
psth_MSR(:,jj) = psth_MSR(:,jj)+psth1;
case 3
meanrate_HSR(:,jj) = meanrate_HSR(:,jj)+r_mean1;
psth_HSR(:,jj) = psth_HSR(:,jj)+psth1;
end
end
end
if ~isempty(fiberTypes), amt_disp(); end
if length(fiberTypes)>1
r_mean(:,jj)=r_mean(:,jj)/length(fiberTypes);
r_var=r_var/length(fiberTypes);
psth(:,jj)=psth(:,jj)/length(fiberTypes);
if nargout >= 7
if kv.numL ~= 0
meanrate_LSR = meanrate_LSR / kv.numL;
psth_LSR(:,jj)=psth_LSR(:,jj)/kv.numL;
end
output.meanrate_LSR = meanrate_LSR;
if kv.numM ~= 0
meanrate_MSR = meanrate_MSR / kv.numM;
psth_MSR(:,jj)=psth_MSR(:,jj)/kv.numM;
end
output.meanrate_MSR = meanrate_MSR;
if kv.numH ~= 0
meanrate_HSR = meanrate_HSR / kv.numH;
psth_HSR(:,jj)=psth_HSR(:,jj)/kv.numH;
end
output.meanrate_HSR = meanrate_HSR;
end
end
% [sum(psth) sum(psth_LSR) sum(psth_MSR) sum(psth_HSR) (4*sum(psth_LSR)+4*sum(psth_MSR)+12*sum(psth_HSR))/20]
end
if ~isempty(kv.psth_binwidth),
bin = round(kv.psth_binwidth*fs);
psth_bin = zeros(size(buffer(psth(:,1),bin),2),numCF);
for ii=1:numCF
psth_bin(:,ii)=sum(buffer(psth(:,ii),bin))/(kv.nrep*kv.psth_binwidth);
end
psth=psth_bin;
if nargout >= 7
%%%
psth_bin = zeros(size(buffer(psth_LSR(:,1),bin),2),numCF);
for ii=1:numCF
psth_bin(:,ii)=sum(buffer(psth_LSR(:,ii),bin))/(kv.nrep*kv.psth_binwidth);
end
psth_LSR=psth_bin;
output.psth_LSR = psth_LSR;
%%%
psth_bin = zeros(size(buffer(psth_MSR(:,1),bin),2),numCF);
for ii=1:numCF
psth_bin(:,ii)=sum(buffer(psth_MSR(:,ii),bin))/(kv.nrep*kv.psth_binwidth);
end
psth_MSR=psth_bin;
output.psth_MSR = psth_MSR;
%%%
psth_bin = zeros(size(buffer(psth_HSR(:,1),bin),2),numCF);
for ii=1:numCF
psth_bin(:,ii)=sum(buffer(psth_HSR(:,ii),bin))/(kv.nrep*kv.psth_binwidth);
end
psth_HSR=psth_bin;
output.psth_HSR = psth_HSR;
end
end
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